1. Field of the Invention
This invention relates generally to the field of data processing systems. More particularly, the invention relates to a system and method for identifying an appropriate demand distribution to use for safety stock planning within a supply chain management (“SCM”) system.
2. Description of the Related Art
Certain software applications are designed to comprehend complicated scheduling tasks. For example, a supply-chain-management (“SCM”) software application is typically designed to comprehend the resources in a supply chain (e.g., raw materials, manufacturing equipment, distribution, warehousing, etc) and schedule their usages (also referred to as “activities”) so that a specific “supply” of product can be provided at one or more places at specific times to meet the anticipated “demand” for the product.
FIG. 1 shows a prior art application server 101 architecture that executes SCM application software 102. The SCM application software 102 includes a demand planning module 103 that is responsible for anticipating the demand for a given product over a specified time period or, more specifically, over a series of successive time periods. By way of example, FIG. 2 illustrates a DP module 101 which has anticipated the demand for a particular product (P1) by a particular customer (C1) for January (100 units), February (150 units) and March (100 units). As indicated in FIG. 2, current demand planning techniques are largely based on empirical data 100 for a given product (e.g., historical demand data stored within an archiving system or other database 105).
In addition, as shown in FIG. 1, the demand planning module 103 employed in certain SCM systems includes a “safety stock planning” component 110 for performing “safety stock” calculations. Safety stock planning is used to safeguard the supply chain against negative effects of uncertain influencing factors such as, for example, errors in predicting demand, disruptions in production, fluctuations of transportation times, etc. Safety stock planning attempts to satisfy demands caused by these factors using an “extra” amount of product, i.e., the so-called “safety stock.” To calculate the amount of safety stock for a given product, current SCM systems evaluate the demand distribution for the product, which describes the possible demand values for the product over specified periods of time (e.g., a day, week, month, etc) and their relative likelihood.
One problem with current safety stock planning solutions, however, is that they are overly simplistic in the manner in which they characterize demand data used for safety stock calculations. For example, in prior systems, the distinction between “regular” demand (typical for products with high and steady demand, like consumer goods) and “sporadic” demand (typical for products with low and uneven demand, like spare parts) is based on one simple condition. That is, if the variation coefficient of the demand distribution is lower than 0.5, then a “regular” demand is assumed for safety stock planning purposes. A Gaussian distribution is the mathematical model used to characterize a “regular” demand. By contrast, if the variation coefficient of the demand distribution is greater than 0.5, then a “sporadic” demand is assumed for safety stock planning purposes. A Gamma distribution is the mathematical model used to characterize a “sporadic” demand. In addition, prior systems do not consider that the period length used in the historical data (e.g., a day) may not match the period length used for safety stock planning (e.g., a week) and that, therefore, the variation coefficient needs to be adjusted accordingly.
In sum, more advanced techniques for performing safety stock planning using demand distribution data are needed.